Effective and reliable robotic delivery systems for handling intermittent, on-demand, or scheduled deliveries of items in a wide variety of environments are needed. Ideally, delivery robots should be able to securely carry objects and remain stable while moving, and have a configuration that prevents object damage or loss.
Robotic forklifts or pallet trucks have been used to move objects. However, such robotic systems are typically limited to factories or warehouses that support a highly structured environment, requiring the use of designated trackways, or having preset physical or electronic guides throughout a building infrastructure to facilitate robot movement without interruption. Such conventional robots can use a minimal set of sensors because obstacle avoidance can be limited to stopping the robot when a mechanical or ultrasonic sensor indicates blocking of the designated trackway until a pathway is cleared.
However, in more complicated environments such as hospitals, hotels, conference facilities, residential facilities, or the like, more sophisticated sensors capable of supporting autonomous navigation are needed. Such sensors can be used to identify and localize absolute or relative position, create maps through simultaneous localization and mapping (SLAM), and detect obstacles such as walls or movable objects. Obstacle detection is particularly important for avoiding unwanted collisions with fast moving vehicles, humans, animals, or even other robots.
Conventional obstacle avoidance commonly relies on long-distance rangefinders that actively scan the environment using laser, infrared, or sonar beams. While such active range finding sensor systems can provide highly accurate centimeter scale position data on millisecond timescales, they are relatively expensive. For example, laser-based sensors with a wide field of view (up to 270°) can sense stationary or moving obstacles up to 30 meters away by projecting a long-range laser beam and scanning it to bounce off any obstacles in order to detect the distance to the closest obstacle along that beam's path. This effectively delivers a view of obstacles in a 270° field of view around the sensor, and provides mapping and/or obstacle avoidance data that can be used by a robot operating system software (ROS), such as that provided by the Open Source Robotics Foundation.
Unfortunately, such sensors are costly and can be difficult to mount and position in a delivery robot. Since sensor systems are a significant component in a robot bill of materials, providing low cost commercial delivery robots depends at least in part upon use of low cost robot sensor systems that are effective, rugged, and simple to calibrate and assemble.